Measuring the knowledge base of regional innovation systems in Germany in terms of a Triple Helix dynamics, Research Policy (forthcoming).

By Loet Leydesdorff, Michael Fritsch. 2006

139 citations recorded by the source corpus.

The interaction among the three sub-dynamics of economic exchange, technological innovation, and institutional control can be captured with a generalized Triple Helix model. We propose to use the information contained in the configuration among the three sub-dynamics as an indicator of the synergy in a configuration. This measure indicates the reduction of the uncertainty which prevails at the level of an innovation system. On the basis of data at the district level in Germany, the conclusions of a previous study about the Netherlands are tested: medium-tech manufacturing is the main driver of the knowledge-based configuration in a regional economy, while knowledge-intensive services tend to uncouple the economy from the regional configuration. At the level of regions (NUTS-2) Germany's knowledge-based economy is no longer structured in terms of the previous East–West divide of the country, while this divide has prevailed at the level of the states (NUTS-1) that constitute the Federal Republic. However, the effects of high and medium-tech are not specific for the western or eastern parts of the country. The configuration of medium-tech manufacturing can be considered a better indicator of the knowledge-based economy than that of high-tech manufacturing.

Subfields: Management of Technology and Innovation, Political Science and International Relations, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/e355a678af8e7a5793fbbece60025d0146a9df66

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In Management of Technology and Innovation

In Political Science and International Relations

In Strategy and Management

Citations in the corpus

The Triple-Helix Model of Smart Cities: A Neo-Evolutionary Perspective (2011, background): “The degree of integration and whether synergy is generated, however, remain empirical questions that are open to measurement (e.g., Leydesdorff and Fritsch, 2006; Leydesdorff and Sun, 2009).”
Evidence for the network perspective on organizational learning (2008, methodology): “In future research, we will propose to measure the self-organization of a topic of research as a reduction of uncertainty which may increase over time ( Leydesdorff & Fritsch, 2006 ).”
The drivers of SME innovation in the regions of the EU (2021, background): “Others have produced quantitative approaches and assessments (e.g., Iammarino, 2005; Leydesdorff and Fritsch, 2006; Fritsch and Slavtchev, 2011).”